Title :
A new state space model for a complex RTRL neural network
Author :
Coelho, Pedro Henrique Gouvêa
Author_Institution :
Dept. of Electron. & Telecommun., State Univ. of Rio de Janeiro, Brazil
Abstract :
The purpose of the work is to represent the complex real time recurrent learning (RTRL) fully recurrent neural network in a state space model for engineering applications such as mobile channel equalization. This representation extends Haykin´s (1999) for complex valued inputs, yielding a compact formulation useful in possible changes in the training of a fully recurrent neural network. Numerical results are presented to illustrate the method
Keywords :
adaptive equalisers; learning (artificial intelligence); mobile radio; recurrent neural nets; time division multiple access; compact formulation; complex real time recurrent learning fully recurrent neural network; complex valued inputs; mobile channel equalization; state space model; Adaptive control; Adaptive equalizers; Communication channels; Equations; Feedback loop; Neural networks; Neurons; Programmable control; Recurrent neural networks; State-space methods;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938427
